When a consumer uses a computing device, they are often exposed to a variety of different products, particularly when browsing the Internet where products are advertised either directly or indirectly. In many cases, a consumer may be exposed to a vast number of products at once. In these instances, the consumer may react positively to a product, but may neglect to move forward with the purchase of said product, sometimes unintentionally. For example, the consumer may be presented with a dozen products at once, one of which attracts their attention, but may be distracted by the overwhelming number of products being presented. In another example, the consumer may be interested in a product via an advertisement, but may be focusing on the web page containing the advertisement and neglect to look into the product, perhaps thinking he or she might do so at a later time.
Targeted advertising mitigates to some degree this problem by presenting advertisements that the consumer is likely to be interested in and reducing the distraction factor. Modern targeted advertising uses demographics and consumer behavior to predict an individual's potential interest, often using powerful computers to automate the process. But what is lacking in a real time or near real time way to detect a consumers interest in a particular product outside of a controlled environment, such as a consumer study group. Currently there is an inability for a computing device to identify when a consumer may be interested in a product that is being viewed in a real-life setting. Standard computing devices lack the technology to measure or identify any reaction on the part of the user, and thus cannot provide assistance to the user in identifying products for purchase. Thus, there is a need for a technological solution to enable a computing device to identify when a consumer is interested in a product based on physiological response and identify the specific product.